INFSCI 2595 Spring 2022: Homework 11

Assigned April 14, 2022; Due: April 21, 2022

Collaborators

Include the names of your collaborators here.

Overview

This is an applied assignment focused on training binary classifiers. You will try out several models discussed in lecture this semester including: logistic regression, elastic net, neural network, random forest, and gradient boosted trees. You will tune the models using caret and then make predictions to study the trends.

IMPORTANT: code chunks are created for you. Each code chunk has eval=FALSE set in the chunk options. You MUST change it to be eval=TRUE in order for the code chunks to be evaluated when rendering the document.

You are allowed to add as many code chunks as you see fit to answer the questions.

Load packages

This assignment will use packages from the tidyverse suite and caret. The models that you will train require their own set of packages. The caret::train() function will prompt you to download the necessary packages. Thus, when you run those code portions please look at the R Console for the prompt that you must respond to. The assignment also requires the mlbench, corrplot, and yardstick packages.

The tidyverse and caret packages are loaded for you below. If you do not have caret please download and install now.

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.6     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.4     ✓ stringr 1.4.0
## ✓ readr   2.1.1     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(caret)

## Loading required package: lattice

## 
## Attaching package: 'caret'

## The following object is masked from 'package:purrr':
## 
##     lift

Read in data

You will work with the Sonar data set in this assignment. The Sonar data set has become a classic data set for training binary classifiers. The data are available in the mlbench package. If you do not have mlbench please download and install it before proceeding.

The code chunk below reads in the Sonar data for you and displays the number of rows and columns to screen.

data("Sonar", package = 'mlbench')

Sonar %>% dim()

## [1] 208  61

As you can see above this a fairly high dimensional problem, especially relative to the number of observations. The input features of the Sonar data set correspond to features extracted from sonar signals (hence the data name). Your task is to classify the binary outcome, Class, as either "M" for metal or "R" for rock.

Problem 01

We will perform a short exploration of the features in this data set. First, let’s look at summary statistics for each of the input variables. The code chunk below creates a long-format dataset for you by gathering the input features into a single column via the pivot_longer() function. The input variable ID is extracted for you and assigned to the input_id column.

lf_sonar <- Sonar %>% 
  tibble::rowid_to_column("obs_id") %>% 
  pivot_longer(!c("obs_id", "Class")) %>% 
  mutate(input_id = as.numeric(stringr::str_extract(name, "\\d+")))

lf_sonar %>% glimpse()

## Rows: 12,480
## Columns: 5
## $ obs_id   <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ Class    <fct> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R…
## $ name     <chr> "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", …
## $ value    <dbl> 0.0200, 0.0371, 0.0428, 0.0207, 0.0954, 0.0986, 0.1539, 0.160…
## $ input_id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…

The above long-format data set will help with a few of the visualizations we will use to explore the data.

1a)

You will start out by visualizing the summary statistics for each input feature via boxplots.

Pipe the lf_sonar object into ggplot(). Map the x aesthetic to input_id and map the y aesthetic to value. Include a geom_boxplot() geom where the group aesthetic is mapped to input_id.

Describe the bounds on the input features. Do the inputs have very different scales?

SOLUTION

###
lf_sonar %>%
  ggplot(mapping = aes(x = input_id, y = value, group = input_id))+
  geom_boxplot()

What do you think?

It looks like inputs have huge variance, but the value of figure is between 0 and 1, which is relative small. Except from those outliers, they looks like similar scales

1b)

Boxplots are useful for visualizing summary statistics. However, we are unable to visualize the shape of the distributions with boxplots. In this problem, you will use frequency polygons (a type of histogram) to visualize the distributional shapes for each input.

Pipe the lf_sonar object into ggplot() and map the x aesthetic to value. Include a geom_freqpoly() geom with the group aesthetic mapped to input_id. Set the number bins equal to 21 by setting bins=21 in the geom_freqpoly() call. Break up your visualizations by groups of 5 inputs by including facet_wrap() where the facet variable is:

cut(input_id, breaks=seq(1, 60, length.out = 12), include.lowest = TRUE)

Set the scales argument in facet_wrap() to be scales='free_y' so it’s easier to see the shapes for each group of inputs.

NOTE: If your facet variable is created correctly, the facet strips will show you the input IDs associated with each facet. For example, the first facet will display [1,6.36] and the last facet will be display (54.6,60]. Although decimal values are shown, input_id contains whole number integers. The decimals are fine for now. It’s just a simple way to break up the variables.

SOLUTION

lf_sonar %>%
  ggplot(mapping = aes(x = value))+
  geom_freqpoly(mapping = aes(group = input_id), bins = 21)+
  facet_wrap(vars(cut(input_id, breaks=seq(1, 60, length.out = 12), include.lowest = TRUE)), 
             scales = 'free_y')

1c)

Based on your visualizations, do you think the input features should be pre-processed? If so, what processing technique should be considered?

SOLUTION

What do you think?

Yes, according to the figure, some of values are extreme. We should apply some pre-processing process. For example, z-score normalization.

1d)

Let’s now consider the correlation structure of the inputs. Use the corrplot::corrplot() function to create the correlation plot matrix associated with all 60 input features. However, rather than using the default ordering, instruct corrplot() to reorder inputs such that all highly correlated inputs are grouped together. corrplot() will use a hierarchical clustering method and group all inputs for you.

Pipe the original wide-format Sonar data into the dplyr::select() function and select all except the Class variable. Pipe the result into cor() and pipe the result into corrplot::corrplot(). Set the method argument equal to "square", the order argument equal to "hclust", and the ‘hclust.methodequal to’ward.D2’`.

Is there a correlation structure between the inputs?

SOLUTION

Is there a correlation structure between the inputs?

Yes, there are a lot of correlated features

Sonar %>%
  dplyr::select(-Class) %>%
  cor() %>%
  corrplot::corrplot(method = "square", order = "hclust", hclust.method = 'ward.D2')

1e)

The last visualization you will make as part of a quick exploratry data analysis (EDA) is to count the number of observations per level of the binary outcome Class. This is important to do before training any binary classification model, in order to check if there is a severe imbalance between either of the two classes.

Pipe the Sonar data set into ggplot() and set the x aesthetic equal to Class. Use the goem_bar() function to show a bar chart giving the number of observations per level of Class.

Do you think we should be concerned about an imbalance between the "M" and "R" levels?

SOLUTION

Sonar %>%
  ggplot(mapping = aes(x = Class))+
  geom_bar()

What do you think?

No we shouldn’t concerned about that, they are not exactly 50 to 50 but still balanced.

Problem 02

You will train several models to predict the binary outcome Class. You will try logistic regression, logistic regression with the elastic net penalty, and more complex non-linear methods. As stated at the beginning of the assignment, you will use caret for the training, tuning, and model selection process.

2a)

You must start, by specifying the resampling scheme and the primary performance metric. You will use 5-fold cross-validation, and you will compare models by maximizing the Area Under the ROC Curve (ROC AUC). You must specify the metric to be "ROC" in order to tell caret to maximize the AUC (the naming convention is a little confusing). The code chunk below is started for you, and provides some arguments to the trainControl() function which are required in order to use "ROC" as the primary performance metric.

Complete the code chunk below. Finish the trainControl() call such that you will use 5-fold cross-validation. Assign "ROC" to the metric_sonar variable.

SOLUTION

ctrl_k05_roc <- trainControl(method = 'cv',
                             number = 5,
                             summaryFunction = twoClassSummary,
                             classProbs = TRUE,
                             savePredictions = TRUE)

metric_sonar <- "ROC"

2b)

You will start with a logistic regression model using linear additive terms for all input features. Remember that the short cut operator . denotes using “everything in the data set”. So you do not have to type out all 60 input variable names in the formula interface. The formula requires the name of the response variable, and so remember that the outcome is the Class variable, not y as in other assignments and lecture examples.

You will use the train() function to train the logistic regression model. You must specify the formula interface, and specify the data to be Sonar. To use the base R logistic regression method in glm(), you must set the method argument equal to "glm". You must also specify the metric argument to be the metric_sonar variable you assigned Problem 2a) in order for caret to identify the best model by maximizing the area under the ROC curve. You must also specify the trControl argument to be the ctrl_k05_roc.

You must set the preProcess argument based on your answer to Problem 1c). If you do not feel you need to pre-process the inputs then you do not need to include the preProcess argument. If you feel you should, then you should set the preProcess argument to your desired pre-processing operation.

Specify the arguments to the train() function in order to train a logistic regression model for the Sonar data set with 5-fold cross-validation and calculate the area under the ROC curve.

The area under the ROC curve is referred to by caret as "ROC". The cross-validation averaged performance merics are printed to the screen for you. What is the area under the ROC curve for your logistic regression model?

HINT: You can ignore warnings displayed during the training of the logistic regression model.

SOLUTION

set.seed(4321)
fit_glm_sonar <- train(Class ~ (.),
                       data = Sonar,
                       method = "glm",
                       metric = metric_sonar,
                       preProcess = c("center", "scale"),
                       trControl = ctrl_k05_roc)

fit_glm_sonar

## Generalized Linear Model 
## 
## 208 samples
##  60 predictor
##   2 classes: 'M', 'R' 
## 
## Pre-processing: centered (60), scaled (60) 
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 166, 167, 165, 167, 167 
## Resampling results:
## 
##   ROC        Sens       Spec     
##   0.7690207  0.7747036  0.7036842

What is the area under the ROC curve for your logistic regression model?

As we can see through the data, the area under ROC curve is 0.7690207

2c)

Since there are relatively few observations based on the number of inputs in Sonar, let’s apply regularization to the linear additive features. Because you are using linear additive terms you may use the shortcut operator . in the formula. The glmnet package will fit a logistic regression model with the elastic net penalty term. You need to set method to "glmnet". The remaining arguments should be consistent to your arguments used in Problem 2b).

Train an elastic net model and tune the hyperparameters with 5-fold cross-validation to maximize the area under the ROC curve. You can use the default tuning grid from caret and so you do NOT need to set the tuneGrid argument.

Should you consider applying preprocessing with the regularized model?

Based on the training results, does the elastic net model favor LASSO or RIDGE more?

SOLUTION

set.seed(4321)
fit_glmnet_sonar <- train(Class ~ .,
                       data = Sonar,
                       method = "glmnet",
                       metric = metric_sonar,
                       preProcess = c("center", "scale"),
                       trControl = ctrl_k05_roc)
fit_glmnet_sonar

## glmnet 
## 
## 208 samples
##  60 predictor
##   2 classes: 'M', 'R' 
## 
## Pre-processing: centered (60), scaled (60) 
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 166, 167, 165, 167, 167 
## Resampling results across tuning parameters:
## 
##   alpha  lambda        ROC        Sens       Spec     
##   0.10   0.0004318733  0.8381527  0.8019763  0.7221053
##   0.10   0.0043187332  0.8567839  0.8118577  0.7526316
##   0.10   0.0431873324  0.8528271  0.7762846  0.7531579
##   0.55   0.0004318733  0.8345278  0.7928854  0.7026316
##   0.55   0.0043187332  0.8543437  0.8118577  0.7426316
##   0.55   0.0431873324  0.8367568  0.8027668  0.7226316
##   1.00   0.0004318733  0.8250947  0.7837945  0.6931579
##   1.00   0.0043187332  0.8511889  0.7940711  0.7321053
##   1.00   0.0431873324  0.8319097  0.8027668  0.7226316
## 
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were alpha = 0.1 and lambda = 0.004318733.

Should you consider applying preprocessing with the regularized model? Based on the training results, does the elastic net model favor LASSO or RIDGE more?

Yes, we should. If alpha = 1, the elastic net will equvalent to LASSO, since the final value of alpha for the model is 0.1, we can we the elastic net is more favor of RIDGE.

2d)

Let’s now try a neural network model with the nnet package. You must specify the method argument equal to "nnet". Non-linear models will attempt to find non-linear relationships between the inputs and the response even if the simple formula Class ~ . is provided. Thus, you do not need to type in all input names to the formula object. The . shortcut of “everything else” is instructing the formula to use “everything else” as an input. You can use the default tuning grid, and so you do not need to specify the tuneGrid argument to train().

Train a neural network binary classifier using the "nnet" package with caret. Does the neural network model achieve a higher area under the ROC curve compared to the elastic net model?

Should you preprocess the inputs before training the neural network?

NOTE: The trace argument is set to FALSE for you already. Otherwise the iteration results are printed. We are focused on the model performance results in this assignment, and so we do not need the iteration results printed to the screen.

SOLUTION

set.seed(4321)
fit_nnet_sonar <- train(Class ~ .,
                        data = Sonar,
                        method = 'nnet',
                        metric = metric_sonar,
                        preProcess = c("center", "scale"),
                        trControl = ctrl_k05_roc,
                        trace = FALSE)

fit_nnet_sonar

## Neural Network 
## 
## 208 samples
##  60 predictor
##   2 classes: 'M', 'R' 
## 
## Pre-processing: centered (60), scaled (60) 
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 166, 167, 165, 167, 167 
## Resampling results across tuning parameters:
## 
##   size  decay  ROC        Sens       Spec     
##   1     0e+00  0.7778292  0.7316206  0.7547368
##   1     1e-04  0.8122769  0.7403162  0.7547368
##   1     1e-01  0.8423237  0.7675889  0.7226316
##   3     0e+00  0.7711421  0.7584980  0.7226316
##   3     1e-04  0.8440862  0.8035573  0.7521053
##   3     1e-01  0.8659455  0.7948617  0.7115789
##   5     0e+00  0.8004421  0.7494071  0.7631579
##   5     1e-04  0.8361234  0.7675889  0.7310526
##   5     1e-01  0.8859871  0.8122530  0.7736842
## 
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were size = 5 and decay = 0.1.

Does the neural network model achieve a higher area under the ROC curve compared to the elastic net model?

Yes, according to the data, the value of ROC column is bigger than before, which represent the area under ROC curve is bigger.

Should you preprocess the inputs before training the neural network?

Yes, in order to standardize the inputs to avoid some problem like exploding gradients.

2e)

Neural networks are challenging to interpret. Several methods have been devised to try and provide interpretibility by providing ways to rank the input variable importance using the weights associated with all hidden units and the output layer.
You may use the default caret provided method for ranking variable importance for the neural network. Plot the variable importances using the default plot method. Then plot the variable importances a second time but set the top argument equal to 20 in the plot() method call. This will limit to the plot to just the top 20 ranked inputs.

What are the top 4 ranked input features as viewed by the neural network?

SOLUTION

Plot all variable importances.

###
plot(varImp(fit_nnet_sonar))

Plot just the top 20 variable importances.

###
plot(varImp(fit_nnet_sonar), top = 20)

What do you think?

V50, V57, V31 and V22

2f)

You will use predictions to help gain further understanding of the influence of the top ranked input features. Two functions are provided for you below which you will use to construct an input grid to make predictions. The first function, make_test_input_list() is defined in the code chunk below. The first argument to make_test_input_list() is a variable name. The second input argument, top_4_inputs, is a character vector. The second input argument will hold the names associated with the top 4 ranked input features. The last input argument, all_data, is a tibble or data.frame. The last input argument is intended to hold the training data set.

The make_test_input_list() function therefore creates a grid of 25 evenly spaced points between the training set min and max bounds for the top 2 ranked inputs. The third and fourth ranked inputs are given 5 unique values, based on specific quantiles of the training set. All other inputs are set to constant values equal to their training set medians.

make_test_input_list <- function(var_name, top_4_inputs, all_data)
{
  xvar <- all_data %>% select(var_name) %>% pull()
  
  if (var_name %in% top_4_inputs[1:2]){
    # use 25 unique values between the min/max values
    xgrid <- seq(min(xvar), max(xvar), length.out = 25)
  } else if (var_name %in% top_4_inputs[3:4]){
    # specify quantiles to use
    xgrid <- quantile(xvar, probs = c(0.05, 0.25, 0.5, 0.75, 0.95), na.rm = TRUE)
    xgrid <- as.vector(xgrid)
  } else {
    # set to their median values
    xgrid <- median(xvar, na.rm = TRUE)
  }
  
  return(xgrid)
}

The second function, make_test_input_grid(), is a wrapper function. It iteratively applies the make_test_input_list() function to all inputs and then uses the expand.grid() function to create full factorial grid based on the list of unique input values.

make_test_input_grid <- function(all_input_names, top_4_inputs, all_data)
{
  test_list <- purrr::map(all_input_names, 
                          make_test_input_list,
                          top_4_inputs = top_4_inputs,
                          all_data = all_data)
  
  expand.grid(test_list, 
              KEEP.OUT.ATTRS = FALSE,
              stringsAsFactors = FALSE) %>% 
    purrr::set_names(all_input_names)
}

You will use the make_test_input_grid() function to create a grid of input values based on the top 4 ranked inputs, as viewed by your tuned neural network model.

Complete the code chunk below by creating two vectors. The first, my_input_names, is a regular vector for the names of the input variables in the Sonar data set. The second, my_top_ranked_inputs, is a regular vector for the names of the top 4 ranked inputs based on your neural network model.

Call the make_test_input_grid() function and assign the result to viz_input_grid. Display the number of rows and columns in viz_input_grid to the screen.

SOLUTION

Add as many code chunk as you feel necessary to complete this problem.

### Initialize parameters
my_input_name <- colnames(Sonar)[-61]
my_top_ranked_inputs <- c("V50","V57","V31","V22")

### Excecute function
viz_input_grid <- make_test_input_grid(my_input_name, my_top_ranked_inputs, Sonar)

## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(var_name)` instead of `var_name` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

### Display result
dim(viz_input_grid)

## [1] 15625    60

Problem 03

We now have everything necessary to make predictions and study behavior. We have a test or visualization grid and we have several models that we can use to consider the influence between the inputs and the response. Since we are working with a binary classification problem, we can view the response two ways. First, we can consider the classification. Second, we can visualize the predicted event probability.

3a)

We will first classify using the neural network model, and then visualize those classifications as a surface plot. Predictions are made with the predict() function. The first argument is the model object and the second argument is the data set we wish to make predictions with.

When we are working with binary classification models, the default result of the predict() function is the class assuming a 50% threshold value.

Make classifications on the test visualization grid using the neural network model and assign the result to the pred_class_nnet object. Check the data type of pred_class_nnet and look at the first few classifications with the head() function.

SOLUTION

pred_class_nnet <- predict(fit_nnet_sonar, viz_input_grid)
typeof(pred_class_nnet)

## [1] "integer"

head(pred_class_nnet)

## [1] M M M M M M
## Levels: M R

3b)

You will now visualize the classification surface based on your neural network model, over your defined test grid.

Pipe viz_input_grid into a mutate() call and assign the pred_class variable equal to pred_class_nnet. Pipe the result into ggplot() where the x aesthetic is your top ranked input and the y aesthetic is your second ranked input. Use a geom_raster() geom to visualize the surface by mapping the fill aesthetic to pred_class. Include facet_grid() to break up the prediction surfaces based on the third and fourth ranked inputs by assigning the vertical facets to the 4th ranked input and the horizontal facets to the 3rd ranked input. Specify the fill by including the scale_fill_brewer() with the palette argument equal to 'Set1'.

SOLUTION

###
viz_input_grid %>%
  mutate(pred_class = pred_class_nnet) %>%
  ggplot(mapping = aes(x = V50, y = V57))+
  geom_raster(mapping = aes(fill = pred_class))+
  facet_grid(cols = vars(V22), rows = vars(V31))+
  scale_fill_brewer(palette = 'Set1')

3c)

You will now make classifications with the elastic net model on the test input grid.

Predict the classifications with the elastic net model and assign the result to the pred_class_enet object.

SOLUTION

###
pred_class_enet <- predict(fit_glmnet_sonar, viz_input_grid)

3d)

Visualize the classification surface based on the elastic net model. Follow the same steps you used to make the surface with the neural network classifications.

How do the visualizations of the classification surface compare with the neural network?

SOLUTION

Classification of elastic net model gives more M result compare to neural network, which means elastic net model is more sensitive, at least for these two parameters.

viz_input_grid %>%
  mutate(pred_class = pred_class_enet) %>%
  ggplot(mapping = aes(x = V50, y = V57))+
  geom_raster(mapping = aes(fill = pred_class))+
  facet_grid(cols = vars(V22), rows = vars(V31))+
  scale_fill_brewer(palette = 'Set1')

3e)

In addition to visualizing the classifications, we can also predict the class probabilities. This allows us to test out our classification threshold, if we would like. It also helps us understand if the predictions are near the threshold boundary.

Class probability predictions are also made with the predict() function. The first argument is still the model object, and the second argument is still the data set we wish to predict. We must include a third argument, type, which instructs the model the “type” of prediction to make. Setting the type argument equal to 'prob' will return the class probabilities, instead of the classification label.

Predict the class probabilities with the neural network model. Assign the result to the pred_prob_nnet object. Display the data type of pred_prob_nnet to the screen, as well as the result of the head() function applied to pred_prob_nnet. How do you know how to access the probability of the 'M' class?

SOLUTION

How do you know how to access the probability of the 'M' class?

By pred_prob_nnet$M

pred_prob_nnet <- predict(fit_nnet_sonar, viz_input_grid, type = 'prob')
typeof(pred_prob_nnet)

## [1] "list"

head(pred_prob_nnet)

##           M           R
## 1 0.9863185 0.013681453
## 2 0.9952580 0.004742048
## 3 0.9981842 0.001815811
## 4 0.9988205 0.001179527
## 5 0.9991026 0.000897417
## 6 0.9748333 0.025166724

3f)

You will now visualize the predicted probability surface for the M class.

Bind the columns of viz_input_grid to the columns in pred_prob_nnet using bind_cols(). Pipe the result into ggplot(). Map the x aesthetic to the top ranked input and the y aesthetic to the second ranked input. Use a geom_raster() geom to visualize the predicted M class surface by mapping the fill aesthetic to M. Include facet_grid() to break up the prediction surfaces based on the third and fourth ranked inputs by assigning the vertical facets to the 4th ranked input and the horizontal facets to the 3rd ranked input.

Specify the fill to be a diverging fill scale by including scale_fill_gradient2(). Set the limits argument to c(0,1). Set the low argument to 'blue', the high argument to 'red', and the mid argument to 'white'. Set the midpoint equal to 0.5. These choices will give you high predicted probabilities that are bright red, with low predicted probabilities as bright blue and 50% probabilities as white. You will therefore be able to clearly see the 50% threshold decision boundary!

SOLUTION

viz_input_grid %>%
  bind_cols(pred_prob_nnet) %>%
  ggplot(mapping = aes(x = V50, y = V57))+
  geom_raster(mapping = aes(fill = M))+
  facet_grid(rows = vars(V31), cols = vars(V22))+
  scale_fill_gradient2(limits = c(0,1), 
                       low = 'blue', 
                       high = 'red', 
                       mid = 'white', 
                       midpoint = 0.5)

Problem 04

Let’s now try out tree-based methods and compare their performance with the elastic net and neural network models.

4a)

We could start with a standard decision tree with CART, but instead we will jump straight to the random forest. To train a random forest model, you must specify method equal to "rf".
You can use the default tuning grid to the mtry tuning parameter.

Do you need to consider preprocessing the inputs for a random forest?

Train a random forest binary classifier by setting the method argument equal to "rf". The code chunk below includes the importance=TRUE argument for you.

What value of mtry was selected as the best, based on the cross-validation results? Why do you think that value was selected?

SOLUTION

Do you need to consider preprocessing the inputs for a random forest?

No. Because tree based model has nothing to do with preprocessing.

What value of mtry was selected as the best, based on the cross-validation results? Why do you think that value was selected?

2 which lead to the highest score of ROC, Sens and Spec.

set.seed(4321)
fit_rf_sonar <- train(Class ~ ., 
                      data = Sonar,
                      method = "rf",
                      metric = metric_sonar,
                      trControl = ctrl_k05_roc,
                      importance = TRUE)

fit_rf_sonar

## Random Forest 
## 
## 208 samples
##  60 predictor
##   2 classes: 'M', 'R' 
## 
## No pre-processing
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 166, 167, 165, 167, 167 
## Resampling results across tuning parameters:
## 
##   mtry  ROC        Sens       Spec     
##    2    0.9374724  0.9193676  0.7636842
##   31    0.9045808  0.8557312  0.7326316
##   60    0.8909954  0.8557312  0.6705263
## 
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 2.

4b)

Now try training a boosted tree model with xgboost. You must set the method argument to xgbTree in order to tell caret to use the XGBoost implementation of the boosted tree algorithm. By default, many different tuning parameters are considered, so instead of printing out the results, the best tuning parameters are printed out for you. Then, the performance results are plotted for you.

Train the XGBoost model with 5-fold cross-validation to maximize the area under the ROC curve.

NOTE: The following code may take a few minutes to complete.

SOLUTION

Train the model.

set.seed(4321)
fit_xgb_sonar <- train(Class ~.,
                       data = Sonar,
                       method = "xgbTree",
                       metric = metric_sonar,
                       trControl = ctrl_k05_roc)

## [23:12:59] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
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## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:00] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:01] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:02] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:03] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:13:04] WARNING: amalgamation/../src/c_api/c_api.cc:785: `ntree_limit` is deprecated, use `iteration_range` instead.
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Print out the best tuning parameters.

fit_xgb_sonar$bestTune

##    nrounds max_depth eta gamma colsample_bytree min_child_weight subsample
## 29     100         2 0.3     0              0.8                1       0.5

Plot the cross-validation results.

plot(fit_xgb_sonar)

4c)

Use the caret variable importance method and plot the top 20 variable importances for the random forest model and the XGBoost model.

Are the top 4 ranked inputs the same as those identified by the neural network?

SOLUTION

Plot the top 20 ranked inputs based on the random forest.

###
plot(varImp(fit_rf_sonar), top = 20)

Plot the top 20 ranked inputs based on XGBoost.

###
plot(varImp(fit_xgb_sonar), top = 20)

Are the top 4 ranked inputs the same as those identified by the neural network?

No, according to figures above, they are not the same.

4d)

Create an input test grid based on the top ranked inputs from the random forest model.

Assign the result to the viz_input_grid_rf object.

SOLUTION

viz_input_grid_rf <- make_test_input_grid(colnames(Sonar)[-61],
                                          c("V11","V12","V9","V10"),
                                          Sonar)
dim(viz_input_grid_rf)

## [1] 15625    60

4e)

Predict the class probabilities with the random forest model for the original neural network based input grid and the new random forest ranked input grid.

Assign the predicted probabilities based on the neural network input grid to pred_prob_rf_on_nnet_grid. Assign the predicted probabilities based on the random forest input grid to pred_prob_rf_on_rf_grid.

SOLUTION

Random forest predictions on the neural network based input grid.

###
pred_prob_rf_on_nnet_grid <- predict(fit_rf_sonar, viz_input_grid, type = 'prob')

Random forest predictions on the random forest based input grid.

###
pred_prob_rf_on_rf_grid <- predict(fit_rf_sonar, viz_input_grid_rf, type = 'prob')

4f)

Visualize the predicted probability surfaces for the M class using the same visualization approach used in Problem 3f).

SOLUTION

Visualize the predicted probability surface based on the neural network grid.

###
viz_input_grid %>%
  bind_cols(pred_prob_rf_on_nnet_grid) %>%
  ggplot(mapping = aes(x = V50, y = V57))+
  geom_raster(mapping = aes(fill = M))+
  facet_grid(rows = vars(V31), cols = vars(V22))+
  scale_fill_gradient2(limits = c(0,1), 
                       low = 'blue', 
                       high = 'red', 
                       mid = 'white', 
                       midpoint = 0.5) 

Visualize the predicted probability surface based on the random forest grid.

###
viz_input_grid %>%
  bind_cols(pred_prob_rf_on_rf_grid) %>%
  ggplot(mapping = aes(x = V50, y = V57))+
  geom_raster(mapping = aes(fill = M))+
  facet_grid(rows = vars(V31), cols = vars(V22))+
  scale_fill_gradient2(limits = c(0,1), 
                       low = 'blue', 
                       high = 'red', 
                       mid = 'white', 
                       midpoint = 0.5)

Problem 05

Now that you have trained and tuned multiple models of varying complexity, it’s time to identify the best performing model.

5a)

The resampling results are compiled together using the resamples() function. You must complete the first code chunk below by assigning the model object to the corresponding name in the list. For example, you must set the fit_glm_sonar object to the GLM variable in the list.

Complete the first code chunk below, by assigning the caret trained model objects to their appropriate variables in the list.

The results are then plotted for you using dotplot() which model is the best?

SOLUTION

According to the figure, RF is the best model.

sonar_roc_compare <- resamples(list(GLM = fit_glm_sonar,
                                    GLMNET = fit_glmnet_sonar,
                                    NNET = fit_nnet_sonar,
                                    RF = fit_rf_sonar,
                                    XGB = fit_xgb_sonar))

dotplot(sonar_roc_compare)

5b)

The code chunk below is completed for you. It extracts the hold-out set predictions associated with the best tuning parameter values for 4 of the models you trained, the logistic regression model, the elastic net, the neural network, and the random forest.

model_pred_results <- fit_rf_sonar$pred %>% tibble::as_tibble() %>% 
  filter(mtry == fit_rf_sonar$bestTune$mtry) %>% 
  select(pred, obs, M, R, rowIndex, Resample) %>% 
  mutate(model_name = "RF") %>% 
  bind_rows(fit_glm_sonar$pred %>% tibble::as_tibble() %>% 
  select(pred, obs, M, R, rowIndex, Resample) %>% 
  mutate(model_name = "GLM")) %>% 
  bind_rows(fit_glmnet_sonar$pred %>% tibble::as_tibble() %>% 
              filter(alpha == fit_glmnet_sonar$bestTune$alpha,
                     lambda == fit_glmnet_sonar$bestTune$lambda) %>% 
              select(pred, obs, M, R, rowIndex, Resample) %>% 
              mutate(model_name = "GLMNET")) %>% 
  bind_rows(fit_nnet_sonar$pred %>% tibble::as_tibble() %>% 
              filter(size == fit_nnet_sonar$bestTune$size,
                     decay == fit_nnet_sonar$bestTune$decay) %>% 
              select(pred, obs, M, R, rowIndex, Resample) %>% 
              mutate(model_name = "NNET"))

The first few rows of the model_pred_results object are displayed for you in the code chunk below.

model_pred_results %>% head()

## # A tibble: 6 × 7
##   pred  obs       M     R rowIndex Resample model_name
##   <fct> <fct> <dbl> <dbl>    <int> <chr>    <chr>     
## 1 M     R     0.624 0.376        3 Fold1    RF        
## 2 R     R     0.494 0.506        5 Fold1    RF        
## 3 R     R     0.294 0.706       10 Fold1    RF        
## 4 R     R     0.104 0.896       11 Fold1    RF        
## 5 R     R     0.498 0.502       16 Fold1    RF        
## 6 M     R     0.63  0.37        20 Fold1    RF

You must use the model_pred_results to plot the ROC curves associated with the best tuning parameter results. You will use the roc_curve() function from the yardstick package to create the ROC curve, just as you did in an earlier assignment. You should have yardstick installed already. If you do not please download and install yardstick before running the code chunk below.

library(yardstick)

## For binary classification, the first factor level is assumed to be the event.
## Use the argument `event_level = "second"` to alter this as needed.

## 
## Attaching package: 'yardstick'

## The following objects are masked from 'package:caret':
## 
##     precision, recall, sensitivity, specificity

## The following object is masked from 'package:readr':
## 
##     spec

Create the cross-validation averaged ROC curve for each of the models you trained. To do so, pipe the model_pred_results objects into group_by() and specify the grouping variable as model_name. Pipe the result to roc_curve() function with obs as the first argument and M as the second argument. Pipe the result to the autoplot() function.

Are your generated ROC curves in aggreement with your cross-validation summary stats displayed before?

SOLUTION

What do you think?

Yes, they correspond to each other, RF is still the best model.

model_pred_results %>%
  group_by(model_name) %>%
  roc_curve(obs, M) %>%
  autoplot()

5c)

It can be useful to get a sense of the variability in the ROC curve, based on the variation in performance across resample folds. You will use the roc_curve() function again, but this time you cannot use autoplot() to create the ROC curve. You will need to use the geom_path() function from ggplot2, to create the figure manually.

Pipe the model_pred_results object to group_by() and specify the grouping variables to be model_name and Resample. Pipe the result to roc_curve() with the first argument assigned as obs and the second argument assigned as M. Pipe the result to ggplot() where you map the x aesthetic to 1 - specificity and the y aesthetic to sensitivity. Add in the geom_path() layer where you map the group color aesthetic to Resample. Use the facet_wrap() function to create separate facets for the models that you trained. However, before your facet_wrap() function include geom_abline() with slope = 1 and intercept = 0 and linetype = 'dotted'. Also include the coord_equa() function. The line and equal coordinates will help make the ROC curve graphic easier to read.

SOLUTION

model_pred_results %>%
  group_by(model_name,Resample) %>%
  roc_curve(obs, M) %>%
  ggplot(aes(x = 1 - specificity, y = sensitivity))+
  geom_path(aes(color = Resample)) + 
  geom_abline(slope = 1, intercept = 0, linetype = 'dotted') + 
  coord_equal() +
  facet_wrap( ~ model_name)